CIS 620: ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE
URL for this page: http://www.cis.upenn.edu/~cis620/home.html
Wednesday 10:30 - 1:30 in Moore 222. Some meetings may run shorter than the full three hours. Special guest lectures may be given at other times and locations.
Dr. Michael Kearns
AT&T Labs, AI Principles Research Department
Local office: Moore 561A
Local phone: (215)573-2821
Email: firstname.lastname@example.org or email@example.com
Office hours: I will generally try to be around for a few hours following class each Wednesday.
The foundations of artificial intelligence have shifted dramatically in the last decade, with probablistic and statistical frameworks for classical problems resulting in new algorithms, analyses and applications. As a direct result of this "probablistic revolution", there is increased coherence between the various subfields of AI. This weekly seminar course will examine a sampling of models and methods in modern AI, including probablistic reasoning (Bayesian networks and graphical models), Markov decision processes and reinforcement learning, machine learning and neural networks, visual processing, and computational neuroscience.
Students should have a firm grasp of basic probability theory and statistics; some background in the analysis of algorithms and the theory of computation is useful but not required.
The course will be run in an informal manner, as a mixture of ``seminar'' and ``reading group'' formats. I will give some lectures, and we will also read and discuss papers together; we may have some outside speakers on particular topics as well. Requirements for registered students are to be determined, but might include periodically being responsible for leading the discussion on a paper or presenting certain portions of it to the class; there may be an occasional exercise as well.
In keeping with the informal nature of the course, this listing will grow and evolve depending on the interests of the participants and our rate of progress. The first two topics below (graphical models and reinforcement learning), however, we will certainly study, and I've already put some of the material we may examine below. I'll try to keep the web page updated to at least reflect what we've covered, and what we're about to cover.
PART I: GRAPHICAL MODELS AND PROBABILISTIC INFERENCE
WEEK 1 (Jan 14): Representing probability distributions by Bayesian networks (directed graphical models);
subtleties of inference in Bayesian networks (``explaining away'');
potential simplifications from hidden variables;
the inference and learning problems; the d-separation criterion for the
conditional independence P(X,Y|E) = P(X|E)P(Y|E).
The following paper has some tutorial material on Bayesian networks, although it is more
oriented towards learning than inference:
WEEK 2 (Jan 21): Review of d-separation; an efficient algorithm for exact inference in
polytrees; variational methods for approximate inference. Please start reading the
Jordan et al. paper below in preparation.
WEEK 3 (Jan 28):
We'll continue an examination of variational methods for approximate
inference, especially as applied to two-layer noisy-OR networks. I
recommend taking a look at the relevant sections of the Jordan et al.
paper above and the Jaakkola and Jordan paper below. We'll also take a
look at an experimental evaluation of the use of such networks for
medical diagnosis; this is the Middleton et al. paper below.
WEEK 4 (Feb 4):
We'll continue examining algorithms for approximate inference
in Bayesian networks. In particular, we'll
take a look at a number of sampling-based approaches, and
also cover some basic material on Markov chains and their
convergence times that will also prove beneficial when we
later study reinforcement learning. I may also describe some
recent work I have been doing with Larry Saul that proves
performance guarantees for algorithms related to the variational
methods, and make some connections with sampling approaches.
Much of the material for this week will be drawn from the two
Radford Neal publications below; the first is a long review,
and I'll stay mainly in Chapters 3 and 4.
WEEK 5 (Feb 11): We will wrap up our study of inference in Bayesian networks with a group discussion of the two papers below. The first defines a stochastic functional programming language that generalizes the models we have been studying, and gives a procedure for inference in distributions defined by programs in this language. The second draws connections between inference procedures in Bayesian networks and the problem of decoding various classical and recent codes for noisy channels.
Please take a look at them in advance of the class, and please bring hard copies with you to class.
ADDITIONAL MATERIAL ON GRAPHICAL MODELS:
A SIMULATOR FOR BAYESIAN NETWORKS (color monitor preferable):
PART II: COMPUTATIONAL NEUROSCIENCE
WEEK 6 (Feb 18): NO CLASS
WEEK 7 (Feb 23): We will dive right in with a paper that touches on many relevant and current topics in Computational Neuroscience, including the specialization of neurons, problems of measurement, reconstruction of stimuli from spike train data, and modeling.
Please bring a hard copy to class with you.
WEEK 8 (Mar 4): Continuing our readings in Computational Neuroscience, we'll cover the following three papers; please ``peruse'' and bring copies to class.
WEEK 9 (Mar 11):
Spring break; no class
WEEK 10 (Mar 18):
We'll finish our studies in computational neuroscience with the following paper.
PART III: MARKOV DECISION PROCESSES AND REINFORCEMENT LEARNING
WEEK 11 (Mar 25):
SPECIAL GUEST LECTURE BY PROF. SATINDER SINGH, U. COLORADO
WEEK 12 (Apr 1):
I'll describe the new E^3 algorithm for reinforcement learning.